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Automated moth flight analysis in the vicinity of artificial light

Published online by Cambridge University Press:  10 May 2018

P. Gaydecki*
Affiliation:
School of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK
*
*Author for correspondence Phone: +44 (0) 161 306 4906 E-mail: patrick.gaydecki@manchester.ac.uk

Abstract

Instrumentation and software for the automated analysis of insect flight trajectories is described, intended for quantifying the behavioural dynamics of moths in the vicinity of artificial light. For its time, this moth imaging system was relatively advanced and revealed hitherto undocumented insights into moth flight behaviour. The illumination source comprised a 125 W mercury vapour light, operating in the visible and near ultraviolet wavelengths, mounted on top of a mobile telescopic mast at heights of 5 and 7.1 m, depending upon the experiment. Moths were imaged in early September, at night and in field conditions, using a ground level video camera with associated optics including a heated steering mirror, wide angle lens and an electronic image intensifier. Moth flight coordinates were recorded at a rate of 50 images per second (fields) and transferred to a computer using a light pen (the only non-automated operation in the processing sequence). Software extracted ground speed vectors and, by instantaneous subtraction of wind speed data supplied by fast-response anemometers, the airspeed vectors. Accumulated density profiles of the track data revealed that moths spend most of their time at a radius of between 40 and 50 cm from the source, and rarely fly directly above it, from close range. Furthermore, the proportion of insects caught by the trap as a proportion of the number influenced by the light (and within the field of view of the camera) was very low; of 1600 individual tracks recorded over five nights, a total of only 12 were caught. Although trap efficiency is strongly dependent on trap height, time of night, season, moonlight and weather, the data analysis confirmed that moths do not exhibit straightforward positive phototaxis. In general, trajectory patterns become more complex with reduced distance from the illumination, with higher recorded values of speeds and angular velocities. However, these characteristics are further qualified by the direction of travel of the insect; the highest accelerations tended to occur when the insect was at close range, but moving away from the source. Rather than manifesting a simple positive phototaxis, the trajectories were suggestive of disorientation. Based on the data and the complex behavioural response, mathematical models were developed that described ideal density distribution in calm air and light wind speed conditions. The models did not offer a physiological hypothesis regarding the behavioural changes, but rather were tools for quantification and prediction. Since the time that the system was developed, instrumentation, computers and software have advanced considerably, allowing much more to be achieved at a small fraction of the original cost. Nevertheless, the analytical tools remain useful for automated trajectory analysis of airborne insects.

Type
Research Papers
Copyright
Copyright © Cambridge University Press 2018 

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References

Angarita-Jaimes, N.C., Parker, J.E.A., Abe, M., Mashauri, F., Martine, J., Towers, C.E., McCall, P.J. & Towers, D.P. (2016) A novel video-tracking system to quantify the behaviour of nocturnal mosquitoes attacking human hosts in the field. Journal of The Royal Society Interface 13, 20150974.Google Scholar
Baker, R.R. & Sadovy, Y. (1978) The distance and nature of the light-trap response of moths. Nature 276, 818821.Google Scholar
Callahan, P.S. (1965) Intermediate and far infrared sensing of nocturnal insects. Part I. Evidences for a far infrared (FIR) electromagnetic theory of communication and sensing in moths and its relationship to the limiting biosphere of the corn earworm. Annals of the Entomological Society of America 58, 727745.Google Scholar
Carver, J.H., Horton, B.H., O'Brien, R.S. & O'Connor, G.G. (1974). The ultraviolet reflectivity of the moon. Earth, Moon, and Planets 9, 295303.Google Scholar
El-Sayed, A.M., Gödde, J. & Arn, H. (2000) A computer-controlled video system for real-time recording of insect flight in three dimensions. Journal of Insect Behavior 13, 881900.Google Scholar
Evangelista, D.J., Ray, D.D., Raja, S.K. & Hedrick, T.L. (2017) Three-dimensional trajectories and network analyses of group behaviour within chimney swift flocks during approaches to the roost. Proceedings of the Royal Society. B 284, 20162602.Google Scholar
Gaydecki, P. (1984) A Quantification of the Behavioural Dynamics of Certain Lepidoptera in Response to Light (PhD thesis), Cranfield Institute of Technology, Cranfield, Bedfordshire, UK.Google Scholar
Hinterwirth, A.J. & Daniel, T.L. (2010) Antennae in the hawkmoth Manduca sexta (Lepidoptera, Sphingidae) mediate abdominal flexion in response to mechanical stimuli. Journal of Comparative Physiology A 196, 947956.Google Scholar
Hsiao, H.S. (1973) Flight paths of night-flying moths to light. Journal of Insect Physiology 19, 19711976.Google Scholar
Ingram, E.M., Augustin, J., Ellis, M.D. & Siegfried, B.D. (2015) Evaluating sub-lethal effects of orchard-applied pyrethroids using video-tracking software to quantify honey bee behaviors. Chemosphere 135, 272277.Google Scholar
Macgregor, C.J., Pocock, M.J.O., Fox, R. & Evans, D.M. (2015), Pollination by nocturnal Lepidoptera, and the effects of light pollution: a review. Ecological Entomology 40, 187198.Google Scholar
Mazokhin-Porshnyakov, G.A. (1961) Why insects fly to light by night. Anzeiger für Schädlingskunde 34, 47.Google Scholar
Mueller, E.A. & Larkin, R.P. (1985) Insects observed using dual-polarization radar. Journal of Atmospheric and Oceanic Technology 2, 4954.Google Scholar
Mullen, E.R., Rutschman, P., Pegram, N., Patt, J.M. & Adamczyk, J.J. (2016) Laser system for identification, tracking, and control of flying insects. Optics Express 24, 1182811838.Google Scholar
Nirmal, A., Sidar, Y. K., Gajbhiye, R. & Laxmi, J. (2017) The effects of moonlight phases on light-trap catches of insects. Journal of Entomology and Zoology Studies 5, 12091210.Google Scholar
Nowinszky, L. & Puskás, J. (2014). Light-trap catch of Lygus sp. (Heteroptera: Miridae) in connection with the polarized moonlight, the collecting distance and the staying of the Moon above horizon. Journal of Advanced Laboratory Research in Biology 5, 102107.Google Scholar
Psychoudakis, D., Moulder, W., Chen, C.C., Zhu, H. & Volakis, J.L. (2008) A portable low-power harmonic radar system and conformal tag for insect tracking. IEEE Antennas and Wireless Propagation Letters 7, 444447.Google Scholar
Reynolds, D.R. & Riley, J.R (2002). Remote-sensing, telemetric and computer-based technologies for investigating insect movement: a survey of existing and potential techniques. Computers and Electronics in Agriculture 35, 271307.Google Scholar
Riley, J.R., Smith, A.D., Reynolds, D.R. & Edwards, A.S. (1996) Tracking bees with harmonic radar. Nature 379, 29.Google Scholar
Robinson, H.S. & Robinson, P.J.M. (1950) Some notes on the observed behavior of Lepidoptera in flight in the vicinity of light sources. Entomologist's Gazette 1, 320.Google Scholar
Schaefer, G.W. & Bent, G.A. (1984) An infra-red remote sensing system for the active detection and automatic determination of insect flight trajectories (IRADIT). Bulletin of Entomological Research 74, 261278.Google Scholar
Shimoda, M. & Honda, K.I. (2013) Insect reactions to light and its applications to pest management. Applied Entomology and Zoology 48, 413421.Google Scholar
Smith, A.D., Riley, J.R. & Gregory, R.D. (1993) A method for routine monitoring of the aerial migration of insects by using a vertical-looking radar. Philosophical Transactions of the Royal Society of London. Series B 340, 393404.Google Scholar
Sokolowski, M.B., Moine, M. & Naassila, M. (2012). ‘Beetrack’: a software for 2D open field locomotion analysis in honey bees. Journal of Neuroscience Methods 207, 211217.Google Scholar
Sotthibandhu, S. & Baker, R.R. (1979) Celestial orientation by the large yellow underwing moth, Noctua pronuba L. Animal Behaviour 27, 786800.Google Scholar
Truxa, C. & Fiedler, K. (2012) Attraction to light-from how far do moths (Lepidoptera) return to weak artificial sources of light? European Journal of Entomology 109, 77.Google Scholar
van Grunsven, R.H., Donners, M., Boekee, K., Tichelaar, I., Van Geffen, K.G., Groenendijk, D., Berendse, F. & Veenendaal, E.M. (2014) Spectral composition of light sources and insect phototaxis, with an evaluation of existing spectral response models. Journal of Insect Conservation 18, 225231.Google Scholar
Warrant, E. & Dacke, M. (2016) Visual navigation in nocturnal insects. Physiology 31, 182192.Google Scholar
Whitehorn, L.J., Hawkes, F.M. & Dublon, I.A. (2013) Superplot3d: an open source GUI tool for 3d trajectory visualisation and elementary processing. Source Code for Biology and Medicine 8, 19.Google Scholar
Wilkinson, D.A., Lebon, C., Wood, T., Rosser, G. & Gouagna, L. C. (2014) Straightforward multi-object video tracking for quantification of mosquito flight activity. Journal of Insect Physiology 71, 114121.Google Scholar
Yela, J.L. & Holyoak, M. (1997) Effects of moonlight and meteorological factors on light and bait trap catches of noctuid moths (Lepidoptera: Noctuidae). Environmental Entomology 26, 12831290.Google Scholar